A shared decision-making tool to support the decision of patients with Lumbar Disc Herniation on whether to undergo surgery: a pilot study

Abstract

This study aims to develop a decision support tool for patients and surgeons dealing with the uncertainty of surgical outcomes and the expectations of both parties for treating symptomatic lumbar disc herniation (LDH). The study uses a mixed-methods approach with Markov Chains (MC) and Analytic Hierarchy Process (AHP) to predict future health states after surgery for LDH, based on patient-reported outcomes measures (PROMs) and custom weights to each individual PROM and elicited priorities. A case-based analysis of two patients is presented to demonstrate the utility of the model in providing a likely trajectory that priority PROMs will follow over time. The study was conducted under the STROBE guidelines as a post hoc analysis of a large spine outcomes research study conducted in southern Brazil. Data were collected from patients operated between 2006 and 2017 to assess pain, disability, mood, and general health status from the preoperative time point to 1 year after surgery using patient-reported outcome questionnaires. The output of the algorithm represented the chances of surgery fulfilling the expectations of Patients A and B. The results presented by the tool suggest that Patient A will have a considerably higher probability of satisfaction and/or not meeting expectations with surgery than Patient B. The study demonstrates the feasibility and utility of a data-driven decision support tool that takes into account patient preferences and beliefs in generating high-quality decision-making and more utility for long-term outcomes in treating symptomatic lumbar disc herniation.